Diabetes Monitoring System in Smart Health Cities Based on Big Data Intelligence
Diabetes is a metabolic disorder in which the body is unable to properly regulate blood sugar levels. It can occur when the body does not produce enough insulin or when cells become resistant to insulin’s effects. There are two main types of diabetes, Type 1 and Type 2, which have different causes a...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , , , |
| منشور في: |
2023
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://depot.sorbonne.ae/handle/20.500.12458/1387 |
| الوسوم: |
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| _version_ | 1857415063918346240 |
|---|---|
| author | AlZu’bi, Shadi |
| author2 | Elbes, Mohammad Mughaid, Ala Bdair, Noor Abualigah, Laith Forestiero, Agostino Abu Zitar, Raed |
| author2_role | author author author author author author |
| author_facet | AlZu’bi, Shadi Elbes, Mohammad Mughaid, Ala Bdair, Noor Abualigah, Laith Forestiero, Agostino Abu Zitar, Raed |
| author_role | author |
| dc.creator.none.fl_str_mv | AlZu’bi, Shadi Elbes, Mohammad Mughaid, Ala Bdair, Noor Abualigah, Laith Forestiero, Agostino Abu Zitar, Raed |
| dc.date.none.fl_str_mv | 2023-02-23T04:53:27Z 2023-02-23T04:53:27Z 2023 |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | 10.3390/fi15020085 1999-5903 https://depot.sorbonne.ae/handle/20.500.12458/1387 10.3390/fi15020085 |
| dc.language.none.fl_str_mv | en |
| dc.relation.none.fl_str_mv | Future Internet |
| dc.subject.none.fl_str_mv | big data intelligence classification data science deep learning E-health healthcare analytics intelligent diagnosis machine learning diabetes prediction |
| dc.title.none.fl_str_mv | Diabetes Monitoring System in Smart Health Cities Based on Big Data Intelligence |
| dc.type.none.fl_str_mv | Controlled Vocabulary for Resource Type Genres::text::periodical::journal::contribution to journal::journal article |
| description | Diabetes is a metabolic disorder in which the body is unable to properly regulate blood sugar levels. It can occur when the body does not produce enough insulin or when cells become resistant to insulin’s effects. There are two main types of diabetes, Type 1 and Type 2, which have different causes and risk factors. Early detection of diabetes allows for early intervention and management of the condition. This can help prevent or delay the development of serious complications associated with diabetes. Early diagnosis also allows for individuals to make lifestyle changes to prevent the progression of the disease. Healthcare systems play a vital role in the management and treatment of diabetes. They provide access to diabetes education, regular check-ups, and necessary medications for individuals with diabetes. They also provide monitoring and management of diabetes-related complications, such as heart disease, kidney failure, and neuropathy. Through early detection, prevention and management programs, healthcare systems can help improve the quality of life and outcomes for people with diabetes. Current initiatives in healthcare systems for diabetes may fail due to lack of access to education and resources for individuals with diabetes. There may also be inadequate follow-up and monitoring for those who have been diagnosed, leading to poor management of the disease and lack of prevention of complications. Additionally, current initiatives may not be tailored to specific cultural or demographic groups, resulting in a lack of effectiveness for certain populations. In this study, we developed a diabetes prediction system using a healthcare framework. The system employs various machine learning methods, such as K-nearest neighbors, decision tree, deep learning, SVM, random forest, AdaBoost and logistic regression. The performance of the system was evaluated using the PIMA Indians Diabetes dataset and achieved a training accuracy of 82% and validation accuracy of 80%. |
| id | sorbonner_ecb2d27506775f495375d8ed0d8f9fb8 |
| identifier_str_mv | 10.3390/fi15020085 1999-5903 |
| language_invalid_str_mv | en |
| network_acronym_str | sorbonner |
| network_name_str | Sorbonne University Abu Dhabi repository |
| oai_identifier_str | oai:depot.sorbonne.ae:20.500.12458/1387 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Diabetes Monitoring System in Smart Health Cities Based on Big Data IntelligenceAlZu’bi, ShadiElbes, MohammadMughaid, AlaBdair, NoorAbualigah, LaithForestiero, AgostinoAbu Zitar, Raedbig data intelligenceclassificationdata sciencedeep learningE-healthhealthcare analyticsintelligent diagnosismachine learningdiabetes predictionDiabetes is a metabolic disorder in which the body is unable to properly regulate blood sugar levels. It can occur when the body does not produce enough insulin or when cells become resistant to insulin’s effects. There are two main types of diabetes, Type 1 and Type 2, which have different causes and risk factors. Early detection of diabetes allows for early intervention and management of the condition. This can help prevent or delay the development of serious complications associated with diabetes. Early diagnosis also allows for individuals to make lifestyle changes to prevent the progression of the disease. Healthcare systems play a vital role in the management and treatment of diabetes. They provide access to diabetes education, regular check-ups, and necessary medications for individuals with diabetes. They also provide monitoring and management of diabetes-related complications, such as heart disease, kidney failure, and neuropathy. Through early detection, prevention and management programs, healthcare systems can help improve the quality of life and outcomes for people with diabetes. Current initiatives in healthcare systems for diabetes may fail due to lack of access to education and resources for individuals with diabetes. There may also be inadequate follow-up and monitoring for those who have been diagnosed, leading to poor management of the disease and lack of prevention of complications. Additionally, current initiatives may not be tailored to specific cultural or demographic groups, resulting in a lack of effectiveness for certain populations. In this study, we developed a diabetes prediction system using a healthcare framework. The system employs various machine learning methods, such as K-nearest neighbors, decision tree, deep learning, SVM, random forest, AdaBoost and logistic regression. The performance of the system was evaluated using the PIMA Indians Diabetes dataset and achieved a training accuracy of 82% and validation accuracy of 80%.2023-02-23T04:53:27Z2023-02-23T04:53:27Z2023Controlled Vocabulary for Resource Type Genres::text::periodical::journal::contribution to journal::journal articleapplication/pdf10.3390/fi150200851999-5903https://depot.sorbonne.ae/handle/20.500.12458/138710.3390/fi15020085enFuture Internetoai:depot.sorbonne.ae:20.500.12458/13872023-06-14T09:36:46Z |
| spellingShingle | Diabetes Monitoring System in Smart Health Cities Based on Big Data Intelligence AlZu’bi, Shadi big data intelligence classification data science deep learning E-health healthcare analytics intelligent diagnosis machine learning diabetes prediction |
| title | Diabetes Monitoring System in Smart Health Cities Based on Big Data Intelligence |
| title_full | Diabetes Monitoring System in Smart Health Cities Based on Big Data Intelligence |
| title_fullStr | Diabetes Monitoring System in Smart Health Cities Based on Big Data Intelligence |
| title_full_unstemmed | Diabetes Monitoring System in Smart Health Cities Based on Big Data Intelligence |
| title_short | Diabetes Monitoring System in Smart Health Cities Based on Big Data Intelligence |
| title_sort | Diabetes Monitoring System in Smart Health Cities Based on Big Data Intelligence |
| topic | big data intelligence classification data science deep learning E-health healthcare analytics intelligent diagnosis machine learning diabetes prediction |
| url | https://depot.sorbonne.ae/handle/20.500.12458/1387 |